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/*
* Copyright (c) 2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE
#define ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE
#include "arm_compute/core/KernelDescriptors.h"
#include "src/gpu/cl/kernels/ClMatMulNativeKernel.h"
#include "tests/CL/CLAccessor.h"
#include "tests/CL/Helper.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/reference/GEMM.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/ReshapeLayer.h"
#include <random>
namespace arm_compute
{
namespace test
{
namespace validation
{
using namespace arm_compute::opencl::kernels;
template <typename T>
class MatMulKernelValidationFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0, bool export_rhs_to_cl_image, DataType data_type)
{
// For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices.
if(pretranspose_a)
{
permute(shape_a, PermutationVector(1U, 0U));
}
if(pretranspose_b)
{
permute(shape_b, PermutationVector(1U, 0U));
}
_device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device());
if(!export_rhs_to_cl_image || _device_supports_export_to_cl_image)
{
_target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type);
_reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type);
}
}
protected:
template <typename U>
void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f)
{
switch(tensor.data_type())
{
case DataType::F16:
{
arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(lo), float(hi) };
library->fill(tensor, distribution, i);
break;
}
case DataType::F32:
{
std::uniform_real_distribution<float> distribution(lo, hi);
library->fill(tensor, distribution, i);
break;
}
default:
library->fill_tensor_uniform(tensor, i);
}
}
CLTensor compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0,
bool export_rhs_to_cl_image, DataType data_type)
{
// Create tensors
CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1);
CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1);
CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1);
CLSynthetizeOperator<ClMatMulNativeKernel> matMul{};
MatMulKernelInfo matmul_info;
matmul_info.adj_lhs = pretranspose_a;
matmul_info.adj_rhs = pretranspose_b;
matmul_info.m0 = M0;
matmul_info.n0 = N0;
matmul_info.k0 = K0;
matmul_info.export_rhs_to_cl_image = export_rhs_to_cl_image;
matMul.configure(a.info(), b.info(), dst.info(), matmul_info);
ARM_COMPUTE_ASSERT(a.info()->is_resizable());
ARM_COMPUTE_ASSERT(b.info()->is_resizable());
ARM_COMPUTE_ASSERT(dst.info()->is_resizable());
// Allocate tensors
a.allocator()->allocate();
b.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
// Fill tensors
fill(CLAccessor(a), 0);
fill(CLAccessor(b), 1);
// Compute matMul kernel
ITensorPack tensors_pack({ { ACL_SRC_0, &a },
{ ACL_SRC_1, &b },
{ ACL_DST, &dst }
});
matMul.run(tensors_pack);
return dst;
}
SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, DataType data_type)
{
// We collapse dimensions > 3 onto dimension 3, i.e. 5D+ tensors will look like 4D
// This is necessary unless we choose to extend gemm reference for 5D+ tensors
TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimW);
TensorShape shape_a_collapsed = shape_a.collapsed_from(Window::DimW);
TensorShape shape_b_collapsed = shape_b.collapsed_from(Window::DimW);
// Create reference
SimpleTensor<T> a{ shape_a_collapsed, data_type, 1 };
SimpleTensor<T> b{ shape_b_collapsed, data_type, 1 };
SimpleTensor<T> c{ output_shape_collapsed, data_type, 1 };
// Fill reference
fill(a, 0);
fill(b, 1);
/* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if pretranspose_A is set to true, then A is assumed to be (B x K x M),
therefore, A must be pre-transposed before passing it to the fixture. And, we transpose A again in the fixture to make it (B x M x K)
in order to be able to call reference implementation that works with (B x M x K) input.
Similarly, if pretranspose_B is set to true, then B is assumed to be (B x N x K), B must be pre-transposed before passing it to the fixture. */
// Define transposed shapes
TensorShape a_transposed_shape(a.shape());
a_transposed_shape.set(0, a.shape().y());
a_transposed_shape.set(1, a.shape().x());
TensorShape b_transposed_shape(b.shape());
b_transposed_shape.set(0, b.shape().y());
b_transposed_shape.set(1, b.shape().x());
// Define transposed tensors
SimpleTensor<T> a_transposed{ a_transposed_shape, data_type };
SimpleTensor<T> b_transposed{ b_transposed_shape, data_type };
// pretranspose a if necessary
if(pretranspose_a)
{
a_transposed = reference::permute<T>(a, PermutationVector(1U, 0U));
}
// pretranspose b if necessary
if(pretranspose_b)
{
b_transposed = reference::permute<T>(b, PermutationVector(1U, 0U));
}
// Setting beta to 0 will effectively disable C for the
// computation of the reference: alpha * A * B + 0 * C
// Use transposed tensors if boolean enabled else use original tensors
SimpleTensor<T> result = reference::gemm<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c, 1.0f, 0.f);
// We reshape the gemm output back if the tensor is high dimensional
if(output_shape_collapsed != output_shape)
{
result = reference::reshape_layer(result, output_shape);
}
return result;
}
CLTensor _target{};
SimpleTensor<T> _reference{};
bool _device_supports_export_to_cl_image { true };
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE */
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